There is a general demand for improved performance in wireless communication systems, and especially in scenarios with many connected users where multi-user transmission modes may be useful. FIG. 1 is a schematic diagram illustrating a simplified example of a network node 10 communicating with multiple users 20.
By way of example, today many wireless standards uses Orthogonal Frequency Division Multiplexing (OFDM) mainly due to the fact that it allows for relatively low complex processing in case of high data rate and high bandwidth where the communication channel is frequency selective. OFDM also allows for a simple way to share the channel between different uses by simply allocating different sets of sub-carriers to different users, known as Orthogonal Frequency-Division Multiple Access (OFDMA).
Another multi-user transmission scheme is based on Multi-User Multiple Input Multiple Output (MU-MIMO).
It is desirable that the allocation of sub-carriers is based on detailed knowledge of the channel conditions for the different users. This kind of allocation of sub-carriers is commonly known as Frequency Selective Scheduling (FSS).
With reference to a particular and non-limiting example of a Wireless Local Area Network (WLAN) system, the network node may be referred to as an Access Point (AP) and the user equipment may be referred to as Station (STA). Naturally, FSS is applicable also to other standards and communication systems.
Although FSS potentially gives a performance gain, it requires that the AP has knowledge of the channels to the different STAs. Such knowledge is typically obtained through channel sounding, i.e., the channels between the AP and the different STAs are measured and based on the obtained measurements, the AP can decide how to allocate sub-carriers to different users. The measured channel knowledge, the Channel State Information (CSI), is then fed back from the STAs to the AP as Channel State Information Feedback (CSI FB).
When trying to optimize the gain that can be obtained by FSS, it is essential to keep the overhead related to obtaining the channel knowledge at the AP at a minimum. Furthermore, there is always latency between the time the channel is measured in the STAs and the time the reported CSI measurement is used in the AP for DL transmissions. During this time, the channel may have changed which means the reported CSI measurement may be obsolete. It's also essential to keep the latency in a range so that the channel does not change significantly during this period.
There are several existing feedback mechanisms that are applicable to system based on Carrier Sensing Multiple Access (CSMA), such as e.g. WLAN. These mechanisms may be applied to obtain CSI for OFDMA transmissions or beamforming.
POLL Feedback
In the IEEE 802.11ac standard, as an example, a feedback mechanism commonly referred to as POLL Feedback has been specified for beamforming. As illustrated in FIG. 2, an AP sends a Null Data Packet Announcement (NDP-A) to initiate feedback by informing the STAs to be ready to measure the channel. The following NDP transmission invokes the CSI from the first STA, i.e. STA1. In the STA the CSI is estimated from the HT-LTF in the preamble portion of the sounding packet, e.g. NDP. The CSI is normally included in a management frame called Action-FB and sent from the STA to the AR Then the AP sends out a Poll packet to obtain CSI from other STAs sequentially. Eventually a multi-user transmission in the downlink is sent from the AP to the STAs based on the CSI received from the STAs.
The overhead of this mechanism includes the transmission of NDP-A, NDP, Poll and Action-FB frames. The latency is relatively short since the transmission is right after the sounding.
There are variations of the Poll feedback mechanism, e.g. the CSI from multiple STAs may be transmitted in one UL-OFDMA frame instead of in the sequential manner.
Reference [1] discloses a method for adapting the CSI feedback rate in multi-user communication systems based on e.g. IEEE 802.11ac. If the feedback rate is too slow, this leads to an inaccurate beamforming, while an excessive feedback rate lead to unnecessary overhead. Therefore, polling messages to STAs with slowly evolving channels may be less frequent, compared to polling messages transmitted to a STA with a faster evolving channel.
Reference [2] relates to a method for collecting CSI, using a downlink Multi-User Multiple Input Multiple Output (MU-MIMO) sounding protocol. There is a large sounding overhead in the 802.11ac protocol, which grows with the number of users in the system. Therefore, the AP determines whether a user is currently being affected by a highly dynamic channel. If the channel is considered stable, no channel sounding is performed before the MU-MIMO transmission.
PIGGYBACK Feedback
With so-called PIGGYBACK feedback, the CSI is sent together with an ACK or Block ACK (BA) frame as shown in FIG. 3. Compared to POLL feedback, the overhead may be less since no NDP-A, NDP and Poll frames are exchanged. To estimate the CSI, the STAs usually measure the High Throughput-Long Training Field (HT-LTF) in the preamble portion of the previous data transmission from the AP. It implies longer latency between the time when the channel is measured and the time when the CSI is actually used. The latency includes the duration of one DL transmission and the channel contention time for the AP to obtain the channel access for the next DL transmission.
In general, FSS requires channel knowledge from STAs that costs transmission overhead. Moreover, the feedback may be obsolete if the latency is too long relative to the channel temporal variation. The existing CSI feedback mechanisms either have shorter latency with higher overhead or vice versa. To achieve FSS gain for DL-OFDMA transmission, CSI from multiple STAs is required which means both the overhead and latency may be higher comparing to a single user case. The same problem applies to beamforming where CSI is also required at the transmitter.
Reference [3] relates to channel prediction in time varying channels. In practical systems, the CSI obtained by feedback channels may be outdated, especially for high mobility users. One way to combat this is through channel prediction, where the CSI at the transmission instance is predicted using a Kalman-filter.
There is a general demand for improvements relating to channel state feedback for multi-user transmission in wireless communication systems.